Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure above 15 mmHg, which was based on pre-operative cardiac CT. The study included 96 patients who underwent a bidirectional Glenn procedure. Key points Twenty-three candidate descriptors were manually extracted from cardiac computed tomography images, and seven of them were selected for subsequent modeling. The random forest model presents the best predictive performance for pulmonary pressure among all methods. The computed tomography-based machine learning model could predict post–Glenn shunt pulmonary pressure non-invasively. Article: Prediction of pulmonary pressure after Glenn shunts by computed tomography-based machine learning models Authors: Lei Huang, Jiahua Li, Meiping Huang, Jian Zhuang, Haiyun Yuan, Qianjun Jia, Dewen Zeng, Lifeng Que, Yue Xi, Jijin Lin & Yuhao Dong